Infrared target tracking with kernel-based performance metric and eigenvalue-based similarity measure

Anti-Interference Aircraft-Tracking Method in Infrared Imagery

In this paper, we focus on developing an algorithm for infrared-imaging guidance that enables the aircraft to be reliably tracked in the event of interference. The key challenge is to track the aircraft with occlusion caused by decoys and drastic appearance changes resulting from a diversity of attacking angles. To address this challenge, an aircraft-tracking algorithm was proposed, which provides robustness in tracking the aircraft against the decoys. We reveal the inherent structure and infrared signature of the aircraft, which are used as discriminative features to track the aircraft. The anti-interference method was developed based on simulated images but validate the effectiveness on both real infrared image sequences without decoys and simulated infrared imagery. For frequent occlusion caused by the decoys, the mechanism of occlusion detection is exploited according to the variation of the model distance in tracking process. To have a comprehensive evaluation of tracking performance, infrared-image sequences with different attack angles were simulated, and experiments on benchmark trackers were performed to quantitatively evaluate tracking performance. The experiment results demonstrate that our aircraft-tracking method performs favorably against state-of-the-art trackers.

Real-time infrared target tracking based on ℓ1 minimization and compressive features

Tracking a target in infrared (IR) sequences is a challenging task because of low resolution, low signal-to-noise ratios, occlusion, and poor target visibility. For many civil and military applications, the realtime requirement is always a key factor for tracking algorithms to be used. This undoubtedly makes tracking in IR sequences more difficult. This paper presents a real-time IR target tracking under complex conditions based on l1 minimization and compressive features. First, we adopt a sparse measurement matrix to project the high-dimensional Harr-like features to low-dimensional features that are applied to the appearance modeling. This appearance model allows significant reduction in the computational cost of the target-tracking phase. Then, the appearance model is introduced into the framework of the popular l1 tracker. Each IR target candidate is represented by the appearance template based on the structure of sparse representation. Finally, the candidate that has the minimum reconstruction error is selected as the tracking result. The proposed tracking method can combine the real-time advantages of the compressive tracking and the robustness of the l1 tracker. Experimental results on challenging IR image sequences including both aerial targets and ground targets show that the proposed algorithm has better robustness and real-time performance in comparison with two state-of-the-art tracking algorithms.

Online tracking of deformable objects under occlusion using dominant points

This paper deals with tracking of deformable objects in the presence of occlusion using dominant point representation of the boundary contour. A novel nonintegral time propagation model for propagating the dominant points is proposed. It uses an initial guess generated from a linear operation and an analytical conjugate gradient approach for online robust learning of the shape deformation and motion model. A scheme is presented to automatically detect and correct the region of large local deformation. In order to deal with occlusion, admissible restrictions on deformation and motion of the object are automatically determined. The proposed method overcomes the need of offline learning and learns the deformation and motion model of the object using very few initial frames of the input video. The performance of the method is demonstrated using varieties of videos of different objects.

Infrared human tracking with improved mean shift algorithm based on multicue fusion

Mean shift has been presented as a well known and efficient algorithm for tracking infrared targets. However, under complex backgrounds, such as clutter, varying illumination, and occlusion, the traditional mean tracking method often converges to a local maximum and loses the real infrared target. To cope with these problems, an improved mean shift tracking algorithm based on multicue fusion is proposed. According to the characteristics of the human in infrared images, the algorithm first extracts the gray and edge cues, and then uses the motion information to guide the two cues to obtain improved motion-guided gray and edge cues that are fused adaptively into the mean shift framework. Finally an automatic model update is used to improve the tracking performance further. The experimental results show that, compared with the traditional mean shift algorithm, the presented method greatly improves the accuracy and effectiveness of infrared human tracking under complex scenes, and the tracking results are satisfactory.